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Scheduling of a class of partial routing FMS in uncertain environments with beam search

Author

Listed:
  • G. Cherif

    (Normandie Université)

  • E. Leclercq

    (Normandie Université)

  • D. Lefebvre

    (Normandie Université)

Abstract

In this paper, incremental computation of schedules for complex discrete event systems in an uncertain environment is studied. Uncertainties are assumed to occur due to uncontrollable events. A particular class of flexible manufacturing systems (FMSs) with partial precedence constraints is proposed where some operations are performed with total precedence constraints and others with full routing flexibility (namely partial routing FMSs). Interruptions may occur due to unavailability of resources and interruption of operations. Such interruptions may deviate the trajectory from the planed schedule. For the modeling of the partial routing FMS, a systematic multi-level formalism based on the hierarchical structuration of the operations is introduced. Then, the risk of deviation is integrated and a new cost function is defined accordingly. Finally, a modified beam search algorithm referred to as generation double filtered beam search algorithm that accelerates the convergence of the method is proposed. The new algorithm is based on a new filtering mechanism that uses the cost function to selectively explore the state space of Petri net model in order to find a control sequence from an initial state to a reference one with a trade-off between performance and robustness. Examples are used to illustrate the efficiency of the proposed scheduling approach.

Suggested Citation

  • G. Cherif & E. Leclercq & D. Lefebvre, 2023. "Scheduling of a class of partial routing FMS in uncertain environments with beam search," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 493-514, February.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01801-3
    DOI: 10.1007/s10845-021-01801-3
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    References listed on IDEAS

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    1. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    2. Lei He & Mathijs Weerdt & Neil Yorke-Smith, 2020. "Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1051-1078, April.
    3. James T. Lin & Chun-Chih Chiu, 2018. "A hybrid particle swarm optimization with local search for stochastic resource allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 481-495, March.
    4. Libin Han & Keyi Xing & Xiao Chen & Fuli Xiong, 2018. "A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1083-1096, June.
    5. Gmys, Jan & Mezmaz, Mohand & Melab, Nouredine & Tuyttens, Daniel, 2020. "A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 814-833.
    6. Sabuncuoglu, I. & Bayiz, M., 1999. "Job shop scheduling with beam search," European Journal of Operational Research, Elsevier, vol. 118(2), pages 390-412, October.
    7. Carlier, Jacques & Rebai, Ismail, 1996. "Two branch and bound algorithms for the permutation flow shop problem," European Journal of Operational Research, Elsevier, vol. 90(2), pages 238-251, April.
    8. Wei Wang & Yingguang Li & Lingling Huang, 2018. "Rule and branch-and-bound algorithm based sequencing of machining features for process planning of complex parts," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1329-1336, August.
    9. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.
    10. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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